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NVIDIA NCA-GENM Exam Syllabus Topics:

NVIDIA Generative AI Multimodal Sample Questions:

1. What is the significance of using a U-Net like architecture in denoising diffusion probabilistic models?

A) To classify input images as noisy or clean.
B) To segment noisy patches in input images.
C) To generate new images from pure noise.
D) To detect noisy objects in input images.

2. You have a dataset containing information about sales performance for different regions in the last ten years.
Which type of data visualization would be most appropriate to compare the sales performance across regions on a year-by-year basis?

A) Bar chart
B) Line chart
C) Pie chart
D) Scatter plot

3. What does 'kernel fusion' refer to in the context of AI model optimization?

A) Applying multiple layers of kernels to improve model accuracy.
B) Using kernel functions to optimize model hyperparameters.
C) Combining multiple kernels into a single kernel for faster computation.
D) Optimizing model inference by reducing the number of computations by pruning.

4. What does 'modality alignment' refer to?

A) Addressing challenges related to missing or incomplete information across different modalities.
B) The process of integrating diverse data types such as text, images, audio, time series, and geospatial information.
C) The integration of pretrained models to perform custom tasks involving different types of data.
D) Aligning different modalities within multimodal data to ensure meaningful connections and associations.

5. In the transformer architecture, what is the purpose of positional encoding?

A) To encode the semantic meaning of each token in the input sequence.
B) To remove redundant information from the input sequence.
C) To add information about the order of each token in the input sequence.
D) To encode the importance of each token in the input sequence.

Solutions: